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   "source": [
    "# Transfer Learning vs Domain Adaptation"
   ]
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  {
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   "source": [
    "## Transfer learning definition.\n",
    "\n",
    "Transfer learning focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks.\n",
    "\n",
    "-- Wikipedia"
   ]
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   "source": [
    "## Domain adaptation definition.\n",
    "\n",
    "Domain adaptation is used when we aim at learning from a source data distribution, a well performing model on a different (but related) target data distribution. For example, an algorithm trained on news-wires might have to adapt to a new dataset of biomedical documents.\n",
    "\n",
    "-- Wikipedia"
   ]
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  {
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   "id": "minus-apparatus",
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   "source": [
    "## In simple terms.\n",
    "\n",
    "See the difference? Transfer learning is used when you want to _transfer_ the knowledge of a model over to another model, while domain adaptation is used when you want to _adapt_ a model to what it has never seen before. Transfer learning is about keeping what a model knows, and domain adaptation is more about making a model work in a new environment."
   ]
  }
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